How to Drive AI-Driven Change

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange

AI is already everywhere in most orgs - just not often in a way that creates consistent, measurable value. The patterns are familiar: scattered experiments, tool bloat, unclear ROI and teams already at capacity. Leaders I talk to daily feel pressure to “do something with AI,” yet worry about time-to-value, vendor/model confidence and change fatigue. What’s getting in the way? 💣 Fragmented usage and no shared operating model 💣 Burnout risk from “one more tool” without workflow integration 💣 Budget scrutiny and skepticism from managers and frontline teams 💣 Sluggish decision cycles because governance is undefined Don't boil the ocean. Pick a spot and drive some momentum and credibility. Keep it tight with a sense of urgency and measurable impact. Here's a practical framework to make traction in the next 90 days: 💡 Align on outcomes, not tools. Pick 3–5 metrics that matter (speed to execution, collaboration drag reduction, campaign throughput, knowledge findability). 💡 Run impact pilots where work already happens. Prioritize low-friction use cases in campaign ops, asset creation, and knowledge management—then standardize what works. 💡 Publish role-specific playbooks. Make it concrete for PMM, demand gen, ops, and BDRs: inputs, prompts, guardrails, and handoffs baked into existing workflows. 💡 Stand up lightweight governance. Define what “good” looks like (accuracy, privacy, auditability), who approves changes, and how exceptions get resolved quickly. 💡 Equip champions and communicate. Give internal advocates a “first 30 days” kit, FAQs, and a simple scorecard to show quick wins and build momentum. 💡 Instrument adoption and ROI. Track efficiency gains, cycle-time reductions, and usage by workflow—not just licenses provisioned. Leaders who treat AI as an execution system—not a side project—move from experimentation to repeatable value, faster. If your team is feeling the strain of tool sprawl and unclear impact, start with clarity, pilots, playbooks, and scorecards—then scale what proves out.

AI Transformation involves multiple layers across technology, people, and processes. Here are the most relevant components for a successful AI transformation at the enterprise level: 1. Strategic Alignment - AI Vision & Goals: Clear definition of how AI supports the organization’s mission. - Executive Sponsorship: Leadership buy-in to drive funding, priorities, and culture. - Use Case Prioritization: Business-driven selection of high-impact, feasible use cases. 2. Data Foundation - Data Strategy: Governance, quality, privacy, and availability planning. - Data Infrastructure: Modern data platforms (data lakes, warehouses, vector databases). - Labeling & Annotation: Especially important for supervised learning and fine-tuning. 3. Technology Stack - Model Layer: Foundation models (e.g., GPT, Claude), custom ML models, MLOps. - Infrastructure: Scalable compute (cloud, on-prem, hybrid), APIs, and edge support. - Integration Layer: Connectors to business systems (ERP, CRM, ITSM, etc.). 4. Talent & Capabilities - Cross-functional Teams: Data scientists, ML engineers, domain experts, and DevOps. - Training & Upskilling: Programs to enable AI literacy and advanced capabilities. - External Partnerships: Vendors, academia, or consultants to bridge capability gaps. 5. Governance & Risk Management - AI Ethics & Policy: Bias mitigation, explainability, and fairness guidelines. - Compliance & Privacy: GDPR, HIPAA, or industry-specific regulations. - AI GRC: Governance, risk, and compliance tailored to AI lifecycle. 6. Operationalization (MLOps / LLMOps) - Model Lifecycle Management: From experimentation to deployment and monitoring. - CI/CD for AI: Automating testing, retraining, and releasing of models. - Monitoring & Evaluation: Observability for performance, drift, and cost. 7. Change Management - Process Reengineering: Adapting or redesigning processes to leverage AI. - Stakeholder Engagement: Ensuring alignment and reducing resistance. - Communication Strategy: Educating stakeholders on impact and benefits. 8. Agentic & Autonomous Systems (for advanced orgs) - Multi-agent Architectures: AI agents interacting with tools, people, and data. - Tool Orchestration: Dynamic use of APIs, functions, and external systems. - Evaluation Frameworks: Guardrails and alignment metrics for autonomy. 💡 My Takeaway AI Transformation is not just about AI. Behind every successful AI initiative lies a robust foundation in data, automation, and cloud infrastructure. Enterprises that treat AI as a siloed capability often stumble—because scalable, reliable, and secure AI requires more than just models. From infrastructure-as-code to MLOps, from data pipelines to secure deployment, true transformation demands an integrated architecture where AI, cloud, and automation work in harmony. 🎯 That’s the mindset I believe in: AI is the tip of the spear—but it's the foundation that makes it fly. #DigitalTransformation #ArtificialIntelligence #EnterpriseAI

Many executive teams don’t know how to lead AI. Billions are being spent. Pilots launched. Demos applauded. Measurable impact? Rare. The constraint isn’t technology. It’s leadership. AI isn’t a tool rollout. It’s an operating model shift. Here are 9 AI capabilities every CXO must master: 1/ Own AI As Business Strategy AI reshapes cost, speed, and margin. If it sits in IT, it stays in pilot mode. Your move: Treat AI like capital allocation. Tie it directly to P&L priorities. 2/ Measure Outcomes, Not Activity Productivity gains don’t equal growth. Hours saved isn’t a board metric. Revenue accelerated. Cycle time reduced. Decisions improved. Your move: Define 3 AI metrics tied to growth or margin. Review monthly. 3/ Run AI Like A Portfolio Most companies launch pilots. Few manage them. Your move: Score initiatives by value, risk, and scalability. Set 90-day checkpoints and kill criteria. 4/ Raise AI Literacy At The Top Executives must evaluate risk, economics, and scalability, not features. Your move: Hold quarterly AI briefings focused on business impact, not vendor demos. 5/ Close The Governance Gap Shadow AI already exists inside your company. Without guardrails, experimentation becomes exposure. Your move: Define what’s allowed, restricted, and review-required. 6/ Redesign Workflows First AI layered onto broken processes only accelerates inefficiency. One services firm cut campaign launch time 42% after rebuilding workflow around AI planning and measurement, not just content. Your move: Ask, “If we built this from scratch with AI, what would it look like?” 7/ Address Workforce Anxiety AI-driven redesign creates uncertainty, especially among managers. Silence amplifies fear. Your move: Be explicit about where AI augments, automates, and how roles evolve. 8/ Choose Partners Strategically Tools don’t create advantage. Integration depth does. Your move: Evaluate vendors on workflow fit and adaptability, not feature lists. 9/ Make AI A Standing Agenda Item Most companies are “using AI.” Few are extracting value. The gap is change management. Your move: Give AI a dedicated leadership slot. Track it like pipeline and capital spend. The truth: AI will widen performance gaps. Those who treat it as workflow redesign and capital strategy will compound advantage. Those who treat it as experimentation will stay in pilot mode. Technology isn’t waiting. Leadership can’t either. Save this for future reference.

𝐀𝐈 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐫𝐚𝐭𝐞𝐬 𝐫𝐞𝐯𝐞𝐚𝐥 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐡𝐞𝐚𝐥𝐭𝐡 𝐭𝐡𝐚𝐧 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐫𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬. A CISO presents an AI project with a strong business case. Six months later, the technology works but sits largely unused. What failed? 𝐓𝐡𝐞 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦: Technology leaders focus on capability and cost. Business cases assume full deployment. But adoption determines ROI, and adoption is an organizational challenge, not a technical one. Most organizations treat change management as a communications exercise. Announce the initiative. Schedule training. Expect adoption. This approach consistently underdelivers because it misunderstands what drives behavior change in technical organizations. 𝐖𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐫𝐢𝐯𝐞𝐬 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧: Map organizational impact before announcing the initiative. Which roles change? Which processes are disrupted? Who loses visibility or control? Address these directly with stakeholders before resistance becomes obstruction. Establish adoption metrics alongside technology metrics. System performance matters, but user engagement and workflow integration determine value. Make adoption rates a board-level metric with the same weight as uptime or security incidents. Invest in change leaders within the organization, not just executive sponsorship. The VP championing the initiative in board meetings matters less than the senior analyst demonstrating value to peers in daily work. 𝐓𝐡𝐞 𝐜𝐨𝐬𝐭 𝐨𝐟 𝐟𝐚𝐢𝐥𝐮𝐫𝐞: Organizations write off functional AI platforms as technology failures when the actual failure is assuming adoption is automatic. The financial cost is the sunk investment. The strategic cost is organizational reluctance to attempt the next necessary transformation. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚��𝐬 𝐟𝐨𝐫 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐥𝐞𝐚𝐝𝐞𝐫𝐬: Planning an AI implementation? Budget meaningful resources for structured change management. Not training sessions. Change management as a discipline with defined objectives, accountability, and measurement.

Unlocking AI Success: Your Roadmap to Data Mastery & Readiness AI isn’t a “nice-to-have” anymore; it’s table stakes for competitive advantage. Yet too many organizations stumble at the start line, armed with ambition and budget but lacking the right data foundation and change-management playbook. Here’s how to bridge that gap: 1. Build a Rock-Solid Data Bedrock: - Data Quality & Governance: Automate validation checks, enforce clear policies, and empower dedicated data stewards. - Unified Platforms: Break down silos with cloud-native lakes and warehouses for real-time access. - Scalable Architecture: Future-proof your stack so it flexes with emerging AI agents and growing workloads. 2. Cultivate an AI-Ready Culture: People, not just technology, fuel transformation. - Leadership Alignment: Run executive workshops to nail down a shared AI vision. - Skill Building: Invest in data literacy, basic machine-learning know-how, and AI ethics. - Cross-Functional Teams: Stand up “AI Tiger Teams” that blend IT, analytics, and business experts. 3. Steer Transformation with Purpose: Digital change requires more than new tools; it demands a holistic strategy. - Strategic Roadmapping: Tie AI initiatives directly to business goals: revenue growth, cost reduction, or customer experience. - Change Management: Highlight early wins, gather feedback, and celebrate champions along the way. - Governance & Ethics: Set up oversight committees to safeguard compliance and responsible AI use. 4. Embrace AI Agents for Operational Excellence: Autonomous agents can revolutionize everything from support to supply-chain. - Use Case Identification: Start small! Think chatbots or predictive-maintenance alerts. - Pilot & Iterate: Launch MVPs, measure performance, and refine relentlessly. - Scale Responsibly: Monitor behaviors and embed guardrails to keep agents aligned with your values. By mastering your data, empowering your people, and marrying strategy with ethics, you turn AI from a buzzword into a business accelerator. Which part of this roadmap will you tackle first? —----------------- Ready to unlock AI success in your organization? Take our free AI Readiness Assessment Test: https://lnkd.in/efsUn89N Ensure you're positioned for AI success.

How can leaders transform their teams to be AI-first? It starts with mindset. An AI-first mindset means: Seeing AI as an opportunity, not a threat. Viewing AI as a tool to augment teams, not just automate tasks. Using AI to reimagine work, not just optimize work. As leaders, it’s on us to build this mindset within our teams. Here are 5 ways we do this at HubSpot: Use AI daily: Lead by example—trust grows when teams see leaders embrace AI themselves. I use it everyday and share very specific use cases with our company on how I use it. Now every leader is doing the same with their teams. The result is that we will have almost everyone in the company use AI daily by the end of year. Apply constraints: Give clear, focused challenges. We kept headcount flat in Support while growing the customer base by 20%+. Result - the team innovated with AI and over achieved the target. Smart constraints drive innovation. Establish tiger teams: Empower small, agile groups to experiment, innovate, and teach the organization. We have AI Tiger teams in every function - they share progress in Slack channels and there is so much energy with small groups experimenting and learning. Be a learn-it-all: Foster a culture of continuous learning. Share openly about successes and failures alike. We have dedicated 2 full days to learning and scaling with AI this quarter as a company - we have lined up great speakers, ways to experiment and gamified learning. Measure progress and share it: Measure which teams are completing learning modules, using AI everyday and share that openly. A little healthy competition goes a long way in driving AI-fluency. AI isn’t just a technology shift. It’s fundamentally reshaping how work gets done—and that requires shifting our mindset first. Leaders who embrace AI now will unlock creativity, performance, and impact. Are you building an AI-first mindset with your team? #Leadership #AI #Innovation #Mindset #FutureOfWork

In this latest Forbes article, I draw a compelling line from Ada Lovelace’s 19th-century foresight to today’s AI-driven enterprise transformations. Lovelace envisioned machines augmenting human creativity—a vision now realized as #generativeAI reshapes industries. Accenture's experience with over 2,000 gen AI projects reveals that only 13% of companies achieve significant enterprise-wide value, while 36% are scaling AI for industry-specific solutions. Success in this new era hinges on more than just technology investment. Companies must also invest in their people, prioritize industry-specific AI applications, and embed responsible AI practices from the outset. Organizations adopting agentic architecture - digital teams comprising orchestrator, super, and utility agents—are 4.5 times more likely to realize enterprise-level value. Here are five key lessons we’ve learned: 1. Lead with value from the top: Executive sponsorship is crucial. Companies with CEO sponsorship achieve 2.5 times higher ROI from their #AI investments.  2. Invest in people, not just technology: Empower your workforce with the skills to harness AI. Organizations excelling in AI transformation invest in broad AI upskilling, adopt dynamic workforce models, and enable human + agent collaboration.  3. Prioritize industry-specific AI solutions: Tailor AI applications to your sector’s unique needs. Companies creating enterprise-level value are 2.9 times more likely to have a comprehensive data strategy to support their AI efforts.  4. Design and embed AI responsibly from the start: Ensure ethical and effective AI integration. Organizations creating enterprise-level value are 2.7 times more likely to have responsible AI principles and governance in place across the AI lifecycle.  5. Reinvent continuously: Stay adaptable in the face of ongoing change. Companies with advanced change capabilities are 2.1 times more likely to achieve successful transformations. These lessons should serve as a practical playbook for navigating the complexities of #AI integration and achieving sustainable growth. Please read the full article to explore how Lovelace’s visionary ideas are shaping the future of business through #generativeAI. https://lnkd.in/gEVzQeRA

AI transformation isn’t something leaders do to employees. It’s something we build with them. At its core, transformation is human. Employees need to feel like co-creators of change — not subjects of it. The most successful AI adoption stories we’ve seen at Microsoft share a common thread: people feel empowered, informed, and part of something meaningful.   As we reach the final step of the Frontier Playbook, here’s how leaders build the culture that carries transformation forward:   💡 Set a CEO tone that is real, inspiring, and clear. Employees want to understand why AI matters and what it will mean for the organization. Honest, grounded communication reduces uncertainty and helps people see their role in the journey.   💡 Connect AI to purpose and possibility. Change feels different when employees understand how AI strengthens the mission, improves experiences, and unlocks time for more meaningful work. Leaders who connect AI to purpose build trust and momentum.   💡 Shift managers from supervision to coaching and experimentation. Managers shape the daily experience of change. When they encourage testing, learning, and curiosity, they help teams build the habits and confidence needed to work differently. This is where continuous improvement becomes continuous experimentation.   💡 Empower employees as co-creators of new ways of working. People adopt what they help design. Involving employees in shaping how AI fits into their work builds ownership and makes new behaviors stick. It turns transformation into a shared journey rather than a top-down directive.   When leaders engage and inspire their workforce with clarity and authenticity, transformation becomes sustainable. People feel aligned, confident, and ready to lean into what AI can make possible for their work – and their growth.

Don't do AI - your business isn’t ready for it. Adopting AI isn't a simple plug-and-play; your operating model needs to shift. As a business leader, you first need to answer the question: "How will the AI-reinvented version of our business look?" Consider this: ➡️ Value Realization 1. Which high-value use-cases could significantly transform our business? 2. How will customer experience evolve with AI implementation? 3. What KPIs will measure the success of AI initiatives? ➡️ People 4. How will roles and responsibilities within our team change as AI takes on repetitive tasks? 5. What new skills and capabilities will our employees need to learn? 6. How will we manage change and employee resistance during AI adoption? ➡️ Process 7. How should our current processes evolve to integrate AI effectively? 8. What process will the business use to approve budgets for AI initiatives? 9. How will we train and inform both technical and non-technical staff about AI? ➡️ Technology 10. What infrastructure upgrades are required to facilitate AI integration? 11. How will we handle data privacy and security in our AI operations? 12. How will IT enable the business function through AI? ➡️ Leadership 13. Which executive will champion our AI Centre of Excellence (CoE)? 14. What will AI governance look like? 15. What strategies will we implement to ensure Responsible AI practices? These questions are your roadmap. Answering them will position your business not just to survive, but to lead in the AI-driven future. 📈 The next step? Start exploring these questions, develop a clear vision, and take strategic action. Companies like Microsoft and JPMorgan have thrived by methodically addressing these questions before scaling AI. Others rushed in with flashy projects that failed to deliver ROI. Embracing AI isn't just advantageous - it's essential, for all businesses. But the way you navigate this transition will determine whether your business thrives or becomes another cautionary tale. Follow for more executive-level insights on navigating AI successfully.